| Unreasonable effectiveness of learning neural networks: From accessible states and robust ensembles to basic algorithmic schemes C Baldassi, C Borgs, JT Chayes, A Ingrosso, C Lucibello, L Saglietti, ... Proceedings of the National Academy of Sciences 113 (48), E7655-E7662, 2016 | 144 | 2016 |
| Subdominant dense clusters allow for simple learning and high computational performance in neural networks with discrete synapses C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Physical review letters 115 (12), 128101, 2015 | 118 | 2015 |
| Gaussian process prior variational autoencoders FP Casale, A Dalca, L Saglietti, J Listgarten, N Fusi Advances in neural information processing systems 31, 2018 | 71 | 2018 |
| Local entropy as a measure for sampling solutions in constraint satisfaction problems C Baldassi, A Ingrosso, C Lucibello, L Saglietti, R Zecchina Journal of Statistical Mechanics: Theory and Experiment 2016 (2), 023301, 2016 | 50 | 2016 |
| Learning may need only a few bits of synaptic precision C Baldassi, F Gerace, C Lucibello, L Saglietti, R Zecchina Physical Review E 93 (5), 052313, 2016 | 29 | 2016 |
| Role of synaptic stochasticity in training low-precision neural networks C Baldassi, F Gerace, HJ Kappen, C Lucibello, L Saglietti, E Tartaglione, ... Physical review letters 120 (26), 268103, 2018 | 22 | 2018 |
| Generalized approximate survey propagation for high-dimensional estimation C Lucibello, L Saglietti, Y Lu International Conference on Machine Learning, 4173-4182, 2019 | 5 | 2019 |
| From statistical inference to a differential learning rule for stochastic neural networks L Saglietti, F Gerace, A Ingrosso, C Baldassi, R Zecchina Interface focus 8 (6), 20180033, 2018 | 5 | 2018 |
| Solvable model for inheriting the regularization through knowledge distillation L Saglietti, L Zdeborová Mathematical and Scientific Machine Learning, 809-846, 2022 | 4 | 2022 |
| Probing transfer learning with a model of synthetic correlated datasets F Gerace, L Saglietti, SS Mannelli, A Saxe, L Zdeborová Machine Learning: Science and Technology 3 (1), 015030, 2022 | 4 | 2022 |
| Large deviations for the perceptron model and consequences for active learning H Cui, L Saglietti, L Zdeborová Mathematical and Scientific Machine Learning, 390-430, 2020 | 3 | 2020 |
| From inverse problems to learning: a statistical mechanics approach C Baldassi, F Gerace, L Saglietti, R Zecchina Journal of Physics: Conference Series 955 (1), 012001, 2018 | 3 | 2018 |
| Large deviations in the perceptron model and consequences for active learning H Cui, L Saglietti, L Zdeborovà Machine Learning: Science and Technology 2 (4), 045001, 2021 | 1 | 2021 |
| An Analytical Theory of Curriculum Learning in Teacher-Student Networks L Saglietti, SS Mannelli, A Saxe arXiv preprint arXiv:2106.08068, 2021 | 1 | 2021 |
| Generalized Approximate Survey Propagation for High-Dimensional Estimation: Supplementary Material L Saglietti, Y Lu, C Lucibello arXiv preprint arXiv:1905.05313, 0 | 1 | |
| Inducing bias is simpler than you think SS Mannelli, F Gerace, N Rostamzadeh, L Saglietti arXiv preprint arXiv:2205.15935, 2022 | | 2022 |
| Inducing bias is simpler than you think S Sarao Mannelli, F Gerace, N Rostamzadeh, L Saglietti arXiv e-prints, arXiv: 2205.15935, 2022 | | 2022 |
| Large deviations of semisupervised learning in the stochastic block model H Cui, L Saglietti, L Zdeborová Physical Review E 105 (3), 034108, 2022 | | 2022 |
| Out of Equilibrium Statistical Physics of Learning L Saglietti Politecnico di Torino, 2018 | | 2018 |
| Group ID U13825 F Behrens, LA Clarte, I Coke, HC Cui, F Gerace, G Piccioli, L Saglietti, ... | | |
Riccardo Zecchinaprofessor, theoretical physics, Bocconi UniversityDirección de correo verificada de unibocconi.it
